Infectious disease transmission between individuals in a heterogeneous population is often best modelled through a contact network. However, such contact network data are often unobserved. Such missing data can be accounted for in a Bayesian data augmented framework using Markov chain Monte Carlo (MCMC). Unfortunately, fitting models in such a framework can be highly computationally intensive. We investigate the fitting of network-based infectious disease models with completely unknown contact networks using approximate Bayesian computation population Monte Carlo (ABC-PMC) methods. This is done in the context of both simulated data, and data from the UK 2001 foot-and-mouth disease epidemic. We show that ABC-PMC is able to obtain reasonable approximations of the underlying infectious disease model with huge savings in computation time when compared to a full Bayesian MCMC analysis.

中文翻译：

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使用近似贝叶斯计算将接触网的不确定性纳入传染病的个体模型中。

异质人口中个体之间的传染病传播通常最好通过接触网络来建模。但是，这样的联系网络数据通常是不可观察的。可以使用马尔可夫链蒙特卡洛（MCMC）在贝叶斯数据增强框架中解决此类丢失的数据。不幸的是，在这样的框架中拟合模型可能需要大量的计算。我们使用近似贝叶斯计算种群蒙特卡洛（ABC-PMC）方法调查完全未知的接触网络的基于网络的传染病模型的拟合。这是在模拟数据和英国2001年口蹄疫流行数据的背景下完成的。